Harnessing the power of artificial intelligence to advance cell therapy.
cell signaling
cell therapy
machine learning
signaling motifs
synthetic biology
Journal
Immunological reviews
ISSN: 1600-065X
Titre abrégé: Immunol Rev
Pays: England
ID NLM: 7702118
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
02
05
2023
accepted:
17
06
2023
medline:
27
11
2023
pubmed:
7
7
2023
entrez:
7
7
2023
Statut:
ppublish
Résumé
Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
147-165Subventions
Organisme : Division of Biological Infrastructure
ID : DBI-1548297
Organisme : IBM Exploratory Life Science Program
ID : #3013
Informations de copyright
© 2023 The Authors. Immunological Reviews published by John Wiley & Sons Ltd.
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